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Author Topic: Modelling the Anthropocene  (Read 29354 times)

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #150 on: August 22, 2017, 05:51:50 PM »
The linked references discusses the level of modeling effort recommended to reduce uncertainty of projecting future changes in the ENSO due to internal variability:

Xiao-Tong Zheng, Chang Hui & Sang-Wook Yeh (2017), "Response of ENSO amplitude to global warming in CESM large ensemble: uncertainty due to internal variability", Climate Dynamics, pp 1–17, https://doi.org/10.1007/s00382-017-3859-7

https://rd.springer.com/article/10.1007%2Fs00382-017-3859-7?utm_content=bufferc17f2&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

Abstract: "El Niño–Southern Oscillation (ENSO) is the dominant mode of variability in the coupled ocean-atmospheric system. Future projections of ENSO change under global warming are highly uncertain among models. In this study, the effect of internal variability on ENSO amplitude change in future climate projections is investigated based on a 40-member ensemble from the Community Earth System Model Large Ensemble (CESM-LE) project. A large uncertainty is identified among ensemble members due to internal variability. The inter-member diversity is associated with a zonal dipole pattern of sea surface temperature (SST) change in the mean along the equator, which is similar to the second empirical orthogonal function (EOF) mode of tropical Pacific decadal variability (TPDV) in the unforced control simulation. The uncertainty in CESM-LE is comparable in magnitude to that among models of the Coupled Model Intercomparison Project phase 5 (CMIP5), suggesting the contribution of internal variability to the intermodel uncertainty in ENSO amplitude change. However, the causations between changes in ENSO amplitude and the mean state are distinct between CESM-LE and CMIP5 ensemble. The CESM-LE results indicate that a large ensemble of ~15 members is needed to separate the relative contributions to ENSO amplitude change over the twenty-first century between forced response and internal variability."
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― Leon C. Megginson

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #151 on: August 31, 2017, 06:20:52 PM »
The linked reference uses a climate model to help quantify the impact of "Earth greening" on global warming & found that global land-surface warming was decreased by about 12% (over the past 30 years) due to this consideration.  This makes me wonder what will happen if/when "Earth greening" moves in the negative direction due to climate and land use stresses on vegetation:

Zhenzhong Zeng et. al. (2017), "Climate mitigation from vegetation biophysical feedbacks during the past three decades", Nature Climate Change  7, 432–436, doi:10.1038/nclimate3299

http://www.nature.com/nclimate/journal/v7/n6/full/nclimate3299.html?foxtrotcallback=true

Abstract: "The surface air temperature response to vegetation changes has been studied for the extreme case of land-cover change; yet, it has never been quantified for the slow but persistent increase in leaf area index (LAI) observed over the past 30 years (Earth greening). Here we isolate the fingerprint of increasing LAI on surface air temperature using a coupled land–atmosphere global climate model prescribed with satellite LAI observations. We find that the global greening has slowed down the rise in global land-surface air temperature by 0.09 ± 0.02 °C since 1982. This net cooling effect is the sum of cooling from increased evapotranspiration (70%), changed atmospheric circulation (44%), decreased shortwave transmissivity (21%), and warming from increased longwave air emissivity (−29%) and decreased albedo (−6%). The global cooling originated from the regions where LAI has increased, including boreal Eurasia, Europe, India, northwest Amazonia, and the Sahel. Increasing LAI did not, however, significantly change surface air temperature in eastern North America and East Asia, where the effects of large-scale atmospheric circulation changes mask local vegetation feedbacks. Overall, the sum of biophysical feedbacks related to the greening of the Earth mitigated 12% of global land-surface warming for the past 30 years."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #152 on: September 12, 2017, 10:16:39 PM »
The linked comparison study provides insights into issues such as those raised by Sherwood et al (2014).  While the reference makes no definitive conclusions, it does highlight the importance of correctly modeling slow response feedback mechanisms associated with the ocean heat uptake (particularly in the (Equatorial Pacific) since 1750:

Kuan-Man Xu et al (9 September 2017), "Differences in the hydrological cycle and sensitivity between multiscale modeling frameworks with and without a higher-order turbulence closure", JAMES, DOI: 10.1002/2017MS000970

http://onlinelibrary.wiley.com/doi/10.1002/2017MS000970/full

Abstract: "Current conventional global climate models (GCMs) produce a weak increase in global-mean precipitation with anthropogenic warming in comparison with the lower tropospheric moisture increases. The motive of this study is to understand the differences in the hydrological sensitivity between two multiscale modeling frameworks (MMFs) that arise from the different treatments of turbulence and low clouds in order to aid to the understanding of the model spread among conventional GCMs. We compare the hydrological sensitivity and its energetic constraint from MMFs with (SPCAM-IPHOC) or without (SPCAM) an advanced higher-order turbulence closure. SPCAM-IPHOC simulates higher global hydrological sensitivity for the slow response but lower sensitivity for the fast response than SPCAM. Their differences are comparable to the spreads of conventional GCMs. The higher sensitivity in SPCAM-IPHOC is associated with the higher ratio of the changes in latent heating to those in net atmospheric radiative cooling, which is further related to a stronger decrease in the Bowen ratio with warming than in SPCAM. The higher sensitivity of cloud radiative cooling resulting from the lack of low clouds in SPCAM is another major factor in contributing to the lower precipitation sensitivity. The two MMFs differ greatly in the hydrological sensitivity over the tropical lands, where the simulated sensitivity of surface sensible heat fluxes to surface warming and CO2 increase in SPCAM-IPHOC is weaker than in SPCAM. The difference in divergences of dry static energy flux simulated by the two MMFs also contributes to the difference in land precipitation sensitivity between the two models."

Edit, see also:

Sherwood, S.C., Bony, S. and Dufresne, J.-L., (2014) "Spread in model climate sensitivity traced to atmospheric convective mixing", Nature; Volume: 505, pp 37–42, doi:10.1038/nature12829

http://www.nature.com/nature/journal/v505/n7481/full/nature12829.html

Fasullo, J.T. and Trenberth, K.E., (2012), "A Less Cloudy Future: The Role of Subtropical Subsidence in Climate Sensitivity", Science, vol. 338, pp. 792-794, 2012. http://dx.doi.org/10.1126/science.1227465.

http://www.sciencemag.org/content/338/6108/792
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #153 on: September 12, 2017, 10:44:09 PM »
The unusually high Pacific trade winds in recent decades is frequently associated with the faux hiatus; and the linked reference discusses bias in CMIP5 projections that resulted in under-predictions of the strength of these Pacific trade winds during the faux hiatus.

Jules B. Kajtar, Agus Santoso, Shayne McGregor, Matthew H. England & Zak Baillie (2017), "Model under-representation of decadal Pacific trade wind trends and its link to tropical Atlantic bias", Climate Dynamics", doi:10.1007/s00382-017-3699-5

https://link.springer.com/article/10.1007/s00382-017-3699-5

Abstract: "The strengthening of the Pacific trade winds in recent decades has been unmatched in the observational record stretching back to the early twentieth century. This wind strengthening has been connected with numerous climate-related phenomena, including accelerated sea-level rise in the western Pacific, alterations to Indo-Pacific ocean currents, increased ocean heat uptake, and a slow-down in the rate of global-mean surface warming. Here we show that models in the Coupled Model Intercomparison Project phase 5 underestimate the observed range of decadal trends in the Pacific trade winds, despite capturing the range in decadal sea surface temperature (SST) variability. Analysis of observational data suggests that tropical Atlantic SST contributes considerably to the Pacific trade wind trends, whereas the Atlantic feedback in coupled models is muted. Atmosphere-only simulations forced by observed SST are capable of recovering the time-variation and the magnitude of the trade wind trends. Hence, we explore whether it is the biases in the mean or in the anomalous SST patterns that are responsible for the under-representation in fully coupled models. Over interannual time-scales, we find that model biases in the patterns of Atlantic SST anomalies are the strongest source of error in the precipitation and atmospheric circulation response. In contrast, on decadal time-scales, the magnitude of the model biases in Atlantic mean SST are directly linked with the trade wind variability response."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

6roucho

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Re: Modelling the Anthropocene
« Reply #154 on: September 13, 2017, 08:14:04 PM »
AnruptSLR, how do you read so much and so widely? You're a machine!

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #155 on: September 14, 2017, 04:22:32 PM »
AnruptSLR, how do you read so much and so widely? You're a machine!

As noted in the Adapting to the Anthropocene thread, by "... the recursive application of: deductive logic, inductive logic, the reduction of entropy, concentration/focus/effort/work and letting go of preconditioning ... "
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #156 on: September 19, 2017, 07:58:38 PM »
Given the complex nature of climate change, I think that CMIP6 and similarly future modeling efforts would do well to follow the advice given in the linked reference to combine "… dynamical modeling with data-driven methodological approaches (i.e., neural networks and Granger causality) …".

Fulvio Mazzocchi & Antonello Pasini (31 May 2017), "Climate model pluralism beyond dynamical ensembles", Wires: Climate Change, DOI: 10.1002/wcc.477

http://onlinelibrary.wiley.com/doi/10.1002/wcc.477/full

Abstract: "Using pluralist research strategies can be a profitable way to study complex systems. This contribution focuses on the approaches for studying the climate that make use of multiple different models, aiming to increase the reliability (in terms of robustness) of attribution results. This Opinion article argues that the traditional approach, which is based on ensemble runs of global climate models, only partially allows the application of a robustness scheme, owing to the difficulty to match or evaluate the conditions required for robustness (i.e., independence or heterogeneity among models). An alternative ‘multi-approach’ strategy is advanced, beyond dynamical modeling but still preserving the idea of model pluralism. Such a strategy, which uses a set of ensembles of different model types by combining dynamical modeling with data-driven methodological approaches (i.e., neural networks and Granger causality), seems to better match the condition of independence. In addition, neural networks and Granger causality lead to achievements in attribution studies that can complement those obtained by dynamical modeling."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson